| Title: | PK for Anesthetic Depth Indicators |
|---|---|
| Description: | Calculate and compare the Anesthetic Depth Indicators PK values in R language The prediction probability (PK) is a widely used tool for the anesthetic depth indicators, which was first proposed by Dr. Warren D. Smith in the paper Warren D. Smith; Robert C. Dutton; Ty N. Smith (1996) <doi:10.1097/00000542-199601000-00005> and Warren D. Smith; Robert C. Dutton; Ty N. Smith (1996) <doi:10.1002/(SICI)1097-0258(19960615)15:11<1199::AID-SIM218>3.0.CO;2-Y>. They provide the Micro xls files to calculate and compare the PK values. This package provide the easy-to-use API to calculate and compare the PK values using the R language. The package's name, pk4adi, is short for "PK for Anesthetic Depth Indicators". |
| Authors: | Feng Jiang [aut, cre], Hua Li [ctb], Mengge Zhang [ctb], Wanlin Chen [ctb], Warren D Smith [ctb], Robert C Dutton [ctb], Ty N Smith [ctb] |
| Maintainer: | Feng Jiang <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.1.3 |
| Built: | 2025-02-06 02:39:22 UTC |
| Source: | https://github.com/xfz329/rpk4adi |
Compute the PK value to Measure the Performance of Anesthetic Depth Indicators.
calculate_pk(x_in, y_in)calculate_pk(x_in, y_in)
x_in |
a vector, the indicator. |
y_in |
a vector, the state. |
a list containing all the matrices and variables during the calculation. The value list$type is "PK", which indicated the list is the return-value of the function calculate_pk(). The type of list$basic is also a list, which contains the most important results of the function. The type of list$matrices is also a list, which contains all the matrices during the calculation. The type of list$details is also a list, which contains all the intermediate variables during the calculation.
Warren D. Smith, Robert C. Dutton, Ty N. Smith; Measuring the Performance of Anesthetic Depth Indicators. Anesthesiology 1996; 84:38–51 doi: https://doi.org/10.1097/00000542-199601000-00005.
Warren D. Smith, Robert C. Dutton, Ty N. Smith; A measure of association for assessing prediction accuracy that is a generalization of nonparametric ROC area. Statistics in Medicine 1996; 15: 1119-1215 doi: https://doi.org/10.1002/(SICI)1097-0258(19960615)15:11<1199::AID-SIM218>3.0.CO;2-Y.
x1 <- c(0, 0, 0, 0, 0, 0) y1 <- c(1, 1, 1, 1, 1, 2) ans1 <- calculate_pk(x1, y1) ## show the most important results. print(ans1$basic) x2 <- c(1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 6) y2 <- c(1, 1, 1, 1, 1, 2, 1, 1, 3, 3, 2, 2, 2, 2, 2, 1, 3, 3, 3, 3, 3, 3, 3, 3) ans2 <- calculate_pk(x2, y2) ## show the full results. print(ans2)x1 <- c(0, 0, 0, 0, 0, 0) y1 <- c(1, 1, 1, 1, 1, 2) ans1 <- calculate_pk(x1, y1) ## show the most important results. print(ans1$basic) x2 <- c(1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 6) y2 <- c(1, 1, 1, 1, 1, 2, 1, 1, 3, 3, 2, 2, 2, 2, 2, 1, 3, 3, 3, 3, 3, 3, 3, 3) ans2 <- calculate_pk(x2, y2) ## show the full results. print(ans2)
Both of the two input have to be the output of the function calculate_pk().
compare_pks(pk1, pk2)compare_pks(pk1, pk2)
pk1 |
a list, the output of the function calculate_pk(). |
pk2 |
a list, the output of the function calculate_pk(). |
a list containing all the variables during the calculation. The value list$type is "PKC", which indicated the list is the return-value of the function compare_pk(). The type of list$group is also a list, which contains the normal distribution test results for the group variables. The type of list$pair is also a list, which contains the t distribution test results for the pair variables. The type of list$details is also a list, which contains all the intermediate variables during the calculation.
Warren D. Smith, Robert C. Dutton, Ty N. Smith; Measuring the Performance of Anesthetic Depth Indicators. Anesthesiology 1996; 84:38–51 doi: https://doi.org/10.1097/00000542-199601000-00005.
Warren D. Smith, Robert C. Dutton, Ty N. Smith; A measure of association for assessing prediction accuracy that is a generalization of nonparametric ROC area. Statistics in Medicine 1996; 15: 1119-1215 doi: https://doi.org/10.1002/(SICI)1097-0258(19960615)15:11<1199::AID-SIM218>3.0.CO;2-Y.
x1 <- c(1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 6) y1 <- c(1, 1, 1, 1, 1, 2, 1, 1, 3, 3, 2, 2, 2, 2, 2, 1, 3, 3, 3, 3, 3, 3, 3, 3) pk1 <- calculate_pk(x_in = x1, y_in = y1) print(pk1$basic) x2 <- c(1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 6) y2 <- c(1, 1, 2, 1, 1, 2, 1, 2, 3, 3, 2, 2, 1, 2, 2, 2, 3, 3, 3, 3, 2, 3, 3, 2) pk2 <- calculate_pk(x_in = x2, y_in = y2) print(pk2$basic) ans <- compare_pks(pk1, pk2) print(ans$group) print(ans$pair)x1 <- c(1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 6) y1 <- c(1, 1, 1, 1, 1, 2, 1, 1, 3, 3, 2, 2, 2, 2, 2, 1, 3, 3, 3, 3, 3, 3, 3, 3) pk1 <- calculate_pk(x_in = x1, y_in = y1) print(pk1$basic) x2 <- c(1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 6) y2 <- c(1, 1, 2, 1, 1, 2, 1, 2, 3, 3, 2, 2, 1, 2, 2, 2, 3, 3, 3, 3, 2, 3, 3, 2) pk2 <- calculate_pk(x_in = x2, y_in = y2) print(pk2$basic) ans <- compare_pks(pk1, pk2) print(ans$group) print(ans$pair)